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To satisfy the requirement of quality control in printing and packaging industry, a sheet counting apparatus is developed, which adopts a line-scan camera to image the fringes of sheet stack and is able to provide a real-time and noncontact measurement of their quantity. With a brief introduction of the system architecture, our main work focuses on the sheet counting algorithms. The basic principle is to identify each sheet profile from the 1-D image with a robust ridge strength measurement. First, a multiscale bi-Gaussian ridge likelihood measurement and a ridge-valley descriptor are utilized to improve adjacent objects detection by increasing local contrast around sheet fringes. Then, a sheet recognition scheme, which integrates a peak detection algorithm and the ridge region criteria for verification, is proposed to discriminate true sheets from the obtained ridgeness measure. According to experiments and tests in real production lines, our apparatus can reach a very high measuring accuracy for printing papers or cards with a thickness not <0.2 mm. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of learning undirected graphical models from data generated according to the Glauber dynamics (also known as the Gibbs sampler). The Glauber dynamics is a Markov chain that sequentially updates individual nodes (variables) in a graphical model and it is frequently used to sample from the stationary distribution (to which it converges given sufficient time). Additionally, the Glauber dynamics is a natural dynamical model in a variety of settings. This paper deviates from the standard formulation of graphical model learning in the literature, where one assumes access to independent identically distributed samples from the distribution. Much of the research on graphical model learning has been directed toward finding algorithms with low computational cost. As the main result of this paper, we establish that the problem of reconstructing binary pairwise graphical models is computationally tractable when we observe the Glauber dynamics. Specifically, we show that a binary pairwise graphical model on p nodes with maximum degree d can be learned in time f(d)p^2\log p , for a function f(d) defined explicitly in this paper, using nearly the information-theoretic minimum number of samples. [ABSTRACT FROM AUTHOR]

This paper is concerned with online coverage of unknown planar environments by a mobile robot of size D operating with a limited energy capacity battery. The battery capacity is represented by the path length L that the robot can travel under a full battery charge. Starting at S, the robot has to cover a planar environment containing unknown obstacles, and return to S upon task completion. During task execution, the robot may return to S at any time to recharge its battery. This paper first describes a battery powered offline coverage methodology, then introduces the battery powered coverage (BPC) algorithm that performs online battery powered coverage using position and local obstacle detection sensors. The performance of the BPC algorithm is measured by its competitiveness, determined by measuring the mobile robot’s total online path length, l, relative to the optimal offline solution lopt. This paper establishes that the BPC algorithm has a competitive performance of l \leq ( L/ D) lopt. This paper additionally establishes a universal lower bound of l \geq \log ( L/ 4 D) lopt over all online battery powered coverage algorithms. Execution example illustrates the usefulness of the BPC algorithm. [ABSTRACT FROM AUTHOR]

Batch measurement of sheet-like product quantity is very common in manufacturing and commercial areas. Particularly, to satisfy the growing requirement of automatic and nondestructive detection, a machine vision system for stacked substrates counting is proposed in this paper. With a brief description of the system architecture and imaging module, the challenges for real data analysis are investigated. Our main contribution is to develop a robust stripe detection algorithm by combining the merits of local template matching and global frequency domain filtering. Besides the peak-valley feature across the stack profile, the collinear prior shape along the substrates is also utilized in a morphological scheme to purify the obtained ridge-line or stripe images, which, finally, benefits the statistical counting output. It is verified in experiments using a diversity of substrate media that our developed system can achieve a high counting accuracy with the detection error not more than 0.01% as the sample thickness varies between 0.05 mm and 0.5 mm. Moreover, the proposed algorithm performs much robustly with various kinds of abnormalities and interferences like irregular piling, media distortion, and foreign contamination. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel rigid-body motion segmentation algorithm that uses randomized voting to assign high scores to correctly estimated models and low scores to wrongly estimated models. This algorithm is based on an epipolar geometrical representation of the camera motion, and computes scores using the distance between the feature point and the corresponding epipolar line. These scores are accumulated and utilized for motion segmentation. To evaluate the efficacy of our algorithm, we conduct a series of experiments using the Hopkins 155 data set and the UdG data set, which are representative test sets for rigid motion segmentation. Among several state-of-the-art data sets, our algorithm achieves the most accurate motion segmentation results and, in the presence of measurement noise, achieves comparable results to the other algorithms. Finally, we analyze why our motion segmentation algorithm works using probabilistic and theoretical analysis. [ABSTRACT FROM AUTHOR]

Increasing levels of system integration pose difficulties in meeting design specifications for high-performance systems. Oftentimes increased complexity, nonlinearity, and multiple tradeoffs need to be handled simultaneously during the design cycle. Since components in such systems are highly correlated with each other, codesign and co-optimization of the complete system are required. Machine learning (ML) provides opportunities for analyzing such systems with multiple control parameters, where techniques based on Bayesian optimization (BO) can be used to meet or exceed design specifications. In this paper, we propose a new BO-based global optimization algorithm titled Two-Stage BO (TSBO). TSBO can be applied to black box optimization problems where the computational time can be reduced through a reduction in the number of simulations required. Empirical analysis on a set of popular challenge functions with several local extrema and dimensions shows TSBO to have a faster convergence rate as compared with other optimization methods. In this paper, TSBO has been applied for clock skew minimization in 3-D integrated circuits and multiobjective co-optimization for maximizing efficiency in integrated voltage regulators. The results show that TSBO is between $2\times $ - $4\times $ faster as compared with previously published BO algorithms and other non-ML-based techniques. [ABSTRACT FROM AUTHOR]

Rate control typically involves two steps: bit allocation and bitrate control. The bit allocation step can be implemented in various fashions depending on how many levels of allocation are desired and whether or not an optimal rate–distortion (R-D) performance is pursued. The bitrate control step has a simple aim in achieving the target bitrate as precisely as possible. In our recent research, we have developed a \lambda -domain rate control algorithm that is capable of controlling the bitrate precisely for High Efficiency Video Coding (HEVC). The initial research showed that the bitrate control in the \lambda -domain can be more precise than the conventional schemes. However, the simple bit allocation scheme adopted in this initial research is unable to achieve an optimal R-D performance reflecting the inherent R-D characteristics governed by the video content. In order to achieve an optimal R-D performance, the bit allocation algorithms need to be developed taking into account the video content of a given sequence. The key issue in deriving the video-content-guided optimal bit allocation algorithm is to build a suitable R-D model to characterize the R-D behavior of the video content. In this paper, to complement the R- \lambda model developed in our initial work, a D- \lambda model is properly constructed to complete a comprehensive framework of \lambda -domain R-D analysis. Based on this comprehensive \lambda -domain R-D analysis framework, a suite of optimal bit allocation algorithms are developed. In particular, we design both picture-level and basic-unit-level bit allocation algorithms based on the fundamental R-D optimization theory to take full advantage of the content-guided principles. The proposed algorithms are implemented in HEVC reference software, and the experimental results demonstrate that they can achieve an obvious R-D performance improvement with a smaller bitrate control error. The proposed bit allocation algorithms have already been adopted by the Joint Collaborative Team on Video Coding and integrated into the HEVC reference software. [ABSTRACT FROM PUBLISHER]

A schedule-based system is a system that operates on or contains within a schedule of events and breaks at particular time intervals. Entities within the system show presence or absence in these events by entering or exiting the locations of the events. Given radio frequency identification (RFID) data from a schedule-based system, what can we learn about the system (the events and entities) through data mining? Which data mining methods can be applied so that one can obtain rich actionable insights regarding the system and the domain? The research goal of this paper is to answer these posed research questions, through the development of a framework that systematically produces actionable insights for a given schedule-based system. We show that through integrating appropriate data mining methodologies as a unified framework, one can obtain many insights from even a very simple RFID dataset, which contains only very few fields. The developed framework is general, and is applicable to any schedule-based system, as long as it operates under certain basic assumptions. The types of insights are also general, and are formulated in this paper in the most abstract way. The applicability of the developed framework is illustrated through a case study, where real world data from a schedule-based system is analyzed using the introduced framework. Insights obtained include the profiling of entities and events, the interactions between entity and events, and the relations between events. [ABSTRACT FROM AUTHOR]

This paper proposes three different distributed event-triggered control algorithms to achieve leader–follower consensus for a network of Euler–Lagrange agents. We first propose two model-independent algorithms for a subclass of Euler–Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work studying event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities, which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information, and bounds on the initial conditions. When the Euler–Lagrange agents have dynamics that include the vector of gravitational potential forces, an adaptive algorithm is proposed. This requires more information about the agent dynamics but allows for the estimation of uncertain parameters associated with the agent self-dynamics. For each algorithm, a trigger function is proposed to govern the event update times. The controller is only updated at each event, which ensures that the control input is piecewise constant and thus saves energy resources. We analyze each controller and trigger function to exclude Zeno behavior. [ABSTRACT FROM AUTHOR]

In this paper, we devise a fast encoding algorithm for triply extended Reed–Solomon codes. The proposed approach requires approximately two XORs per bit, which improves the prior result of three XORs per bit established by certain maximum distance separable (MDS) array codes. We also prove that, for MDS codes with two and three parities, the scheduling algorithms require at least two XORs per bit. To the best of our knowledge, this is the first provable scheduling algorithm for the triple-parity MDS codes to approach the theoretical lower bounds. The implementation with SIMD instructions is provided. The simulations show that the proposed approach is competitive, as compared with other cutting edge implementations. [ABSTRACT FROM AUTHOR]

In this paper, we design a robust resource allocation algorithm for a wireless-powered communication network (WPCN) taking into account residual hardware impairments (HWIs) at the transceivers, the imperfectness of the channel state information, and the non-linearity of practical radio frequency energy harvesting circuits. In order to ensure power-efficient secure communication, physical layer security techniques are exploited to deliberately degrade the channel quality of a multiple-antenna eavesdropper. The resource allocation algorithm design is formulated as a non-convex optimization problem for minimization of the total power consumption in the network, while guaranteeing the quality of service of the information receivers in terms of secrecy rate. The globally optimal solution of the optimization problem is obtained via a 2-D search and semidefinite programming relaxation. To strike a balance between computational complexity and system performance, a low-complexity iterative suboptimal resource allocation algorithm is also proposed. Numerical results demonstrate that both the proposed optimal and suboptimal schemes can significantly reduce the total system power consumption required for guaranteeing secure communication, and unveil the impact of HWIs on the system performance: 1) residual HWIs create a system performance bottleneck in WPCN in the high transmit/receive power regimes; 2) increasing the number of transmit antennas can effectively reduce the power consumption of wireless power transfer and alleviate the performance degradation due to residual HWIs; and 3) imperfect CSI exacerbates the impact of residual HWIs, which increases the power consumption of both wireless power and wireless information transfer. [ABSTRACT FROM AUTHOR]

Flow-based microfluidic biochips are attracting increasing attention with successful applications in biochemical experiments, point-of-care diagnosis, etc. Existing works in design automation consider the flow-layer design and control-layer design separately, lacking a global optimization and hence resulting in degraded routability and reliability. This paper presents a novel integrated physical co-design methodology, which seamlessly integrates the flow-layer and control-layer design stages. In the flow-layer design stage, a sequence-pair-based placement method is presented, which allows for an iterative placement refinement based on routing feedbacks. In the control-layer design stage, the minimum cost flow formulation is adopted to further improve the routability. Besides that, effective placement adjustment strategies are proposed to iteratively enhance the solution quality of the overall control-layer design. Experimental results show that compared with the existing work, the proposed design flow obtains an average reduction of 40.44% in flow-channel crossings, 31.95% in total chip area, and 22.02% in total flow-channel length. Moreover, all the valves are successfully routed in the control-layer design stage. [ABSTRACT FROM PUBLISHER]

Object recognition is widely used in vision computing for various applications. Traditional CPU and application specific integrated circuit for vision computing cannot provide high performance and enough flexibility, which limit the use of vision systems. In this paper, a hierarchical and parallel pipelined heterogeneous chip for object recognition is proposed to achieve high flexibility, high performance, and area efficiency. In addition, a reformulation of 3D position estimation is proposed. The method uses single precision to achieve the short computing time and accuracy requirement. The hardware resource is small. Application-specific components, such as connected component information extractor and information extraction accelerator, are designed for high performance. Reconfiguration processors and application-specific instruction set processor are introduced to improve flexibility. These components are connected to hierarchical parallel buses. The chip is fabricated in 180-nm CMOS technology and occupies 72.25 mm2 with 1.09M bits on-chip memory. It delivers 204 GOPS + 665M FLOPS operations. The results show that this hierarchical and parallel pipelined heterogeneous chip is suitable for embedded vision systems. [ABSTRACT FROM AUTHOR]

Filtered orthogonal frequency division multiplexing (F-OFDM) system is a promising waveform for 5G and beyond to enable the multi-service system and spectrum efficient network slicing. However, the performance for F-OFDM systems has not been systematically analyzed in the literature. In this paper, we first establish a mathematical model for an F-OFDM system and derive the conditions to achieve the interference-free one-tap channel equalization. In the practical cases (e.g., insufficient guard interval, asynchronous transmission, and so on), the analytical expressions for inter-symbol interference, inter-carrier interference, and adjacent-carrier interference are derived, where the last term is considered as one of the key factors for asynchronous transmissions. Based on the framework, an optimal power compensation matrix is derived to make all of the subcarriers having the same ergodic performance. Another key contribution of this paper is that we propose a multi-rate F-OFDM system to enable low-complexity low-cost communication scenarios, such as narrow-band Internet of Things, at the cost of generating inter-subband interference (ISubBI). Low computational complexity algorithms are proposed to cancel the ISubBI. The result shows that the derived analytical expressions match the simulation results, and the proposed ISubBI cancelation algorithms can significantly save the original F-OFDM complexity (up to 100 times) without significant performance loss. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate neutron-induced errors in three implementations of sort algorithms (QuickSort, MergeSort, and RadixSort) executed on modern graphics processing units designed for high-performance computing and large server applications. We measure the radiation-induced error rate of sort algorithms taking advantage of the neutron beam available at the Los Alamos Neutron Science Center facility. We also analyze output error criticality by identifying specific output error patterns. We found that radiation can cause wrong elements to appear in the sorted array, misalign values as well as application crashes or system hangs. This paper presents results showing that the criticality of the radiation-induced output error pattern depends on the application. Additionally, an extensive fault-injection campaign has been performed. This campaign allows for better understanding of the observed phenomena. We take advantage of SASS-assembly Intrumentator Fault Injector developed by NVIDIA, which can inject faults into all the user-accessible architectural state. Comparing fault-injection results with radiation experiments data provides an understanding that not all the output errors observed under radiation can be replicated in fault injection. However, fault injection is useful in identifying possible root causes of the output errors observed in radiation testing. Finally, we take advantage of our experimental and analytical study to design efficient experimentally tuned hardening strategies. We detect the error patterns that are critical to the final application and find the more efficient way to detect them. With an overhead as low as 16% of the execution time, we are able to reduce the output error rate of sort of about one order of magnitude. [ABSTRACT FROM PUBLISHER]

Lift-off variation causes errors in the eddy current thickness measurements of metallic plates. In this paper, we have developed an algorithm that can compensate for this variation and produce an index that is linked to the thickness, but is virtually independent of lift-off. This index, termed as the compensated peak frequency, can be obtained from the measured multifrequency inductance spectral data using the algorithm we developed in this paper. This method has been derived through mathematical manipulation and verified by both the simulation and experimental data. Accuracy in the thickness measurements at different lift-offs proved to be within 2%. [ABSTRACT FROM AUTHOR]

This paper proposes a new framework for the design of transmit and receive beamformers for interference alignment (IA) without symbol extensions in multi-antenna cellular networks. We consider IA in a G cell network with K users/cell, N antennas at each base station (BS) and M antennas at each user. The proposed framework is developed by recasting the conditions for IA as two sets of rank constraints, one on the rank of interference matrices, and the other on the transmit beamformers in the uplink. The interference matrix consists of all the interfering vectors received at a BS from the out-of-cell users in the uplink. Using these conditions and the crucial observation that the rank of interference matrices under alignment can be determined beforehand, this paper develops two sets of algorithms for IA. The first part of this paper develops rank minimization algorithms for IA by iteratively minimizing a weighted matrix norm of the interference matrix. Different choices of matrix norms lead to reweighted nuclear norm minimization (RNNM) or reweighted Frobenius norm minimization (RFNM) algorithms with significantly different per-iteration complexities. Alternately, the second part of this paper devises an alternating minimization (AM) algorithm where the rank-deficient interference matrices are expressed as a product of two lower-dimensional matrices that are then alternately optimized. Simulation results indicate that RNNM, which has a per-iteration complexity of a semidefinite program, is effective in designing aligned beamformers for proper-feasible systems with or without redundant antennas, while RFNM and AM, which have a per-iteration complexity of a quadratic program, are better suited for systems with redundant antennas. [ABSTRACT FROM PUBLISHER]

Low-dose computed tomography (LDCT) images are often highly degraded by amplified mottle noise and streak artifacts. Maintaining image quality under low-dose scan protocols is a well-known challenge. Recently, sparse representation-based techniques have been shown to be efficient in improving such CT images. In this paper, we propose a 3D feature constrained reconstruction (3D-FCR) algorithm for LDCT image reconstruction. The feature information used in the 3D-FCR algorithm relies on a 3D feature dictionary constructed from available high quality standard-dose CT sample. The CT voxels and the sparse coefficients are sequentially updated using an alternating minimization scheme. The performance of the 3D-FCR algorithm was assessed through experiments conducted on phantom simulation data and clinical data. A comparison with previously reported solutions was also performed. Qualitative and quantitative results show that the proposed method can lead to a promising improvement of LDCT image quality. [ABSTRACT FROM PUBLISHER]

Algebraic-geometric (AG) codes have good error-correction capability due to their generally large code word length. However, their decoding remains complex, preventing practical applications. Addressing the challenge, this paper proposes two interpolation-based low-complexity Chase (LCC) decoding algorithms for one of the most popular AG codes-Hermitian codes. By choosing $\eta $ unreliable symbols and realizing them with the two most likely decisions, $2^\eta $ decoding test-vectors can be formulated. The first LCC algorithm performs interpolation for the common elements of the test-vectors, producing an intermediate outcome that will be shared by the uncommon element interpolation. It eliminates the redundant computation for decoding each test-vector, resulting in a low-complexity. With an interpolation multiplicity of one, the decoding is further facilitated by removing the requirement of pre-calculating the Hermitian curve’s corresponding coefficients. The second LCC algorithm is an adaptive variant of the first algorithm, where the number of test-vectors is determined by the reliability of received information. When the channel condition improves, it can reduce the complexity without compromising the decoding performance. Simulation results show that the both LCC algorithms outperform a number of existing algebraic decoding algorithms for Hermitian codes. Finally, our complexity analysis will reveal the proposals’ low-complexity feature. [ABSTRACT FROM PUBLISHER]

In this paper, a novel augmented complex-valued least-mean kurtosis (ACLMK) algorithm is proposed for processing complex-valued signals. The negated kurtosis of the complex-valued error signal is defined as a cost function by using augmented statistics. As a result of the minimization of this cost function, the ACLMK algorithm containing all second-order statistical properties is obtained for processing noncircular complex-valued signals. Moreover, in this paper, convergence and misadjustment conditions of the proposed ACLMK algorithm are derived from the steady-state analysis. The simulation results on complex-valued system identification, prediction, and adaptive noise cancelling problems show that the use of the cost function defined by the negated kurtosis of the complex-valued error signal based on augmented statistics enables the processing of the noncircular complex-valued signals, and significantly improves the performance of the proposed ACLMK algorithm in terms of the mean square deviation, the mean square error, the prediction gain and the convergence rate when compared to other algorithms. [ABSTRACT FROM AUTHOR]

Location-aware wireless networks can provide precise location information in harsh environments, however, which is only possible when all nodes are well-functioning. In this paper, we propose algorithms and analyze the performance limits for both non-cooperative and cooperative localization networks in the presence of ranging outliers. Especially, we show that the localization performance can be boosted by using the cooperative outlier detection scheme. An algorithm based on expectation-maximization is proposed for non-cooperative localization networks, while a variational message passing-based algorithm is proposed for the cooperative counterparts. Performance limits are investigated using Cramér–Rao lower bound. Further inspection on the performance limits confirms the performance gain from the cooperative detection scheme. Stochastic geometric analysis is also carried out to account for the stochastic nature of wireless networks, as well as to provide simpler expressions and additional insights. Simulation results corroborate the analytical results, and show that both of the proposed algorithms are capable of attaining the corresponding performance limits at a significantly reduced computational cost compared with existing algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, we study the user pairing in a downlink non-orthogonal multiple access (NOMA) network, where the base station allocates the power to the pairwise users within the cluster. In the considered NOMA network, a user with poor channel condition is paired with a user with good channel condition, when both their rate requirements are satisfied. Specifically, the quality of service for weak users can be guaranteed, since the transmit power allocated to strong users is constrained following the concept of cognitive radio. A distributed matching algorithm is proposed in the downlink NOMA network, aiming to optimize the user pairing and power allocation between weak users and strong users, subject to the users’ targeted rate requirements. Our results show that the proposed algorithm outperforms the conventional orthogonal multiple access scheme and approaches the performance of the centralized algorithm, despite its low complexity. In order to improve the system’s throughput, we design a practical adaptive turbo trellis coded modulation scheme for the considered network, which adaptively adjusts the code rate and the modulation mode based on the instantaneous channel conditions. The joint design work leads to significant mutual benefits for all the users as well as the improved system throughput. [ABSTRACT FROM PUBLISHER]

To save energy and alleviate interference, connected dominating set (CDS) was proposed to serve as a virtual backbone of wireless sensor networks (WSNs). Because sensor nodes may fail due to accidental damages or energy depletion, it is desirable to construct a fault tolerant virtual backbone with high redundancy in both coverage and connectivity. This can be modeled as a $k$ -connected $m$ -fold dominating set (abbreviated as $(k,m)$ -CDS) problem. A node set $C\subseteq V(G)$ is a $(k,m)$ -CDS of graph $G$ if every node in $V(G)\backslash C$ is adjacent with at least $m$ nodes in $C$ and the subgraph of $G$ induced by $C$ is $k$ -connected. Constant approximation algorithm is known for $(3,m)$ -CDS in unit disk graph, which models homogeneous WSNs. In this paper, we present the first performance guaranteed approximation algorithm for $(3,m)$ -CDS in a heterogeneous WSN. In fact, our performance ratio is valid for any topology. The performance ratio is at most $\gamma $ , where $\gamma =\alpha +8+2\ln (2\alpha -6)$ for $\alpha \geq 4$ and $\gamma =3\alpha +2\ln 2$ for $\alpha <4$ , and $\alpha $ is the performance ratio for the minimum $(2,m)$ -CDS problem. Using currently best known value of $\alpha $ , the performance ratio is $\ln \delta +o(\ln \delta )$ , where $\delta $ is the maximum degree of the graph, which is asymptotically best possible in view of the non-approximability of the problem. Applying our algorithm on a unit disk graph, the performance ratio is less than 27, improving previous ratio 62.3 by a large amount for the $(3,m)$ -CDS problem on a unit disk graph. [ABSTRACT FROM PUBLISHER]

In spectral analysis, achieving coherent sampling, especially when signals have large distortion, has been a challenge for many years. This paper introduces three algorithms to resolve this issue. In comparison to previous algorithms, and two widely used methods in industry: windowing and four-parameter sine wave fit, these new algorithms are capable of obtaining accurate spectral results of the signal, while achieving high accuracy as well as computational efficiency. The novel contribution of this paper is that not only does it propose two new algorithms, but also it analyzes their advantages and limitations in detail, providing their trade-offs and different fields of applications. Extensive simulations and measurements are performed to validate these algorithms. Combined with their high accuracy, computational efficiency, and robustness against signal purity, these algorithms are readily available to be implemented for bench or on-chip testing. In addition, it is suitable for data converter spectral testing when noncoherent sampling is present, and spectrally pure test signal source is not available. [ABSTRACT FROM PUBLISHER]

The backpressure routing policy is known to be a throughput optimal policy that supports any feasible traffic demand, but may have poor delay performance when packets traverse loops in the network. In this paper, we study loop-free backpressure routing policies that forward packets along directed acyclic graphs (DAGs) to avoid the looping problem. These policies use link reversal algorithms to improve the DAGs in order to support any achievable traffic demand. For a network with a single commodity, we show that a DAG that supports a given traffic demand can be found after a finite number of iterations of the link-reversal process. We use this to develop a joint link-reversal and backpressure routing policy, called the loop free backpressure (LFBP) algorithm. This algorithm forwards packets on the DAG, while the DAG is dynamically updated based on the growth of the queue backlogs. We show by simulations that such a DAG-based policy improves the delay over the classical backpressure routing policy. We also propose a multicommodity version of the LFBP algorithm and via simulation show that its delay performance is better than that of backpressure. [ABSTRACT FROM PUBLISHER]

Algorithmic multiported memory supports concurrent accesses by cooperating block RAMs (BRAMs) with algorithmic operations, and demonstrates the better performance per resource usage on FPGA when compared with register-based designs. However, the current approaches still use significant amount of FPGA resources and pose great design challenges when increasing the access ports. This paper proposes HB-NTX with a resource efficient hierarchical banking structure for nontable-based multi-ported memory design on FPGA. The regular design style enables a systematic flow to scale both read and write ports. When compared with the previous approaches, HB-NTX can reduce 62.03% BRAMs when composing a 2R4W memory with 32K depth. This paper further extends the HB-NTX to alleviate the complexity of the table-based memory designs. When compared with the previous table-based TBLVT approach, the proposed design for a 2R4W memory with 8K depth attains the cost reduction of 39.9%, 14.3%, and 15.6%, for registers, lookup tables, and BRAMs, respectively. [ABSTRACT FROM AUTHOR]

Analog CMOS integrated circuits (ICs) designs depend typically on designer knowledge and experience, as such problems are multivariate and multiobjective, presenting many combinations of input variables to be investigated in order to meet the required specifications. Nowadays, the a posteriori approach is widely used to perform the optimization processes of analog CMOS ICs using evolutionary algorithms. However, these techniques are not totally able to explore potential solutions in specific regions of the Pareto front. Designers then have difficulty in choosing the best solution capable of achieving all desired specifications simultaneously among all the ones found. In this context, the a priori approach using fitness functions has become an important alternative method to overcome these issues of the a posteriori methodology. This paper aims to compare different fitness function profiles used in the a priori optimization processes to boost the effectiveness of the search processes in relation to robustness, accuracy, and yield in analog CMOS ICs designs. We show that the Gaussian profile, proposed here, applied to the lower limit, center value, and upper limit fitness functions is able to improve all the a priori optimization evolutionary techniques investigated, including the genetic, imperialist competitive, and shuffled frog leaping algorithms. [ABSTRACT FROM PUBLISHER]

Liveness of Petri nets means all activities in a modeled system can potentially take place and thus implies deadlock freedom. The research on liveness analysis approaches is inadequate. This paper proposes a liveness judgment reachability graph (LJRG) approach to analyze the liveness of \omega -independent Petri nets. Such nets can be loosely explained as a class of unbounded Petri nets in which the changes of tokens in unbounded places are not related to each other. This paper proposes several algorithms to transform a new modified reachability tree to a new modified reachability graph and then transform it to an LJRG. It then develops the application of LJRG into the liveness analysis of \omega -independent unbounded Petri nets. The proposed method provides a new theoretical method and important tool for the liveness analysis of unbounded Petri nets. It is illustrated via some examples. [ABSTRACT FROM AUTHOR]

In professional/consumer domains, video databases are broadly applied, facilitating quick searching by fast region analysis, to provide an indication of the video contents. For realtime and cost-efficient implementations, it is important to develop algorithms with high accuracy and low computational complexity. In this paper, we analyze the accuracy and computational complexity of newly developed approaches for semantic region labeling and salient region detection, which aim at extracting spatial contextual information from a video. Both algorithms are analyzed by their native DSP computations and memory usage to prove their practical feasibility. In the analyzed semantic region labeling approach, color and texture features are combined with their related vertical image position to label the key regions. In the salient region detection approach, a discrete cosine transform (DCT) is employed, since it provides a compact representation of the signal energy and the computation can be implemented at low cost. The techniques are applied to two complex surveillance use cases, moving ships in a harbor region and moving cars in traffic surveillance videos, to improve scene understanding in surveillance videos. Results show that our spatial contextual information methods quantitatively and qualitatively outperform other approaches with up to 22% gain in accuracy, while operating at several times lower complexity. [ABSTRACT FROM AUTHOR]

In this paper, we present new parallel polynomial multiplication formulas which result in subquadratic space complexity. The schemes are based on a recently proposed block recombination of polynomial multiplication formula. The proposed two-way, three-way, and four-way split polynomial multiplication formulas achieve the smallest space complexities. Moreover, by providing area-time tradeoff method, the proposed formulas enable one to choose a parallel formula for polynomial multiplication which is suited for a design environment. [ABSTRACT FROM AUTHOR]

In many applications, top-k dominating query is an important operation to return k tuples with the highest domination scores in a potentially huge data space. It is analyzed that the existing algorithms have their performance problems when performed on massive data. This paper proposes a novel table-scan-based TDTS algorithm to efficiently compute top-k dominating results. TDTS first presorts the table for early termination. The early termination checking is proposed in this paper, along with the theoretical analysis of scan depth. The pruning operation for tuples is devised in this paper. The theoretical pruning effect shows that the number of tuples maintained in TDTS can be reduced substantially. The extensive experimental results, conducted on synthetic and real-life data sets, show that TDTS outperforms the existing algorithms significantly. [ABSTRACT FROM PUBLISHER]

In this paper, a novel algorithm “firework optimization” (FO) is introduced, as well as its hybridization with generating set search (GSS). FO is an exploration algorithm, which intends to explore a vast region within the search space in order to determine regions that may contain a regional optimum. As the regions with potential are defined, GSS, the exploitation algorithm is applied to determine whether regions determined by FO contain a local or a global optimum. The validity of FO and GSS hybridization algorithm is verified using Branin and Six-Hump Camel test functions, and the hybridization algorithm is applied to an interior permanent magnet synchronous motor, in order to minimize torque ripple. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient reliability-based optimization algorithm based on adaptive dynamic Taylor Kriging (ADTK). In the ADTK model, the basis functions are optimally selected by the binary particle swarm optimization algorithm and the minimal number of sampling data with a desired fitting error is decided. To evaluate performance of reliability calculation, the Monte Carlo simulation method is employed, where the performance constraint functions are constructed by the ADTK surrogate model. Overall, an electromagnetic application-superconducting magnetic energy storage device (TEAM problem 22) is used to investigate performances of the proposed method. [ABSTRACT FROM AUTHOR]

Neuro-inspired architectures based on synaptic memory arrays have been proposed for on-chip acceleration of weighted sum and weight update in machine/deep learning algorithms. In this paper, we developed NeuroSim, a circuit-level macro model that estimates the area, latency, dynamic energy, and leakage power to facilitate the design space exploration of neuro-inspired architectures with mainstream and emerging device technologies. NeuroSim provides flexible interface and a wide variety of design options at the circuit and device level. Therefore, NeuroSim can be used by neural networks (NNs) as a supporting tool to provide circuit-level performance evaluation. With NeuroSim, an integrated framework can be built with hierarchical organization from the device level (synaptic device properties) to the circuit level (array architectures) and then to the algorithm level (NN topology), enabling instruction-accurate evaluation on the learning accuracy as well as the circuit-level performance metrics at the run-time of online learning. Using multilayer perceptron as a case-study algorithm, we investigated the impact of the “analog” emerging nonvolatile memory (eNVM)’s “nonideal” device properties and benchmarked the tradeoffs between SRAM, digital, and analog eNVM-based architectures for online learning and offline classification. [ABSTRACT FROM AUTHOR]

Although advanced motion vector prediction (AMVP) modes based on motion estimation (ME) are selected significantly less due to the merge mode newly adopted in the high-efficiency video coding (HEVC), integer ME (IME) still occupies a large amount of computation in HEVC because the HEVC supports a highly flexible block partitioning structure. Introduction of the merge mode in HEVC substantially affects the optimal search algorithm of IME. Therefore, this gives a chance to reduce the computational complexity of ME with marginal drop of the compression efficiency. This paper proposes a new IME algorithm that significantly reduces its search ranges. Computational complexity of the proposed algorithm is reduced without serious degradation in coding efficiency by obtaining additional accurate motion vector predictors (MVPs) in a bottom-up order and searching narrow regions around multiple MVPs. These bottom-up MVPs are obtained from the prediction units (PUs) in the coding units (CUs) in the hierarchically lower level, which share the pixel area either completely or partially with the current PU. Although the encoder should perform the proposed IME in a bottom-up order from the smallest CUs to the larger CUs, several other processes, such as fractional ME and merges mode are performed in a top-down order to exploit several fast algorithms already adopted in the HEVC test model encoder. To keep compatibility with HEVC standard, the bottom-up MVPs are utilized for only IME. The bitstream is generated using standard AMVP. According to the simulation results, it is confirmed that the proposed algorithm improves the accuracy of MVPs, which leads to the reduction of IME computational complexity up to 81.95% on average with the Bjøntegaard delta bitrate of 0.39%. [ABSTRACT FROM AUTHOR]

As an important power system protection device, the condition of metal oxide surge arresters (MOSAs) in service could be monitored, and online monitoring might be considered in a few critical installations. In this paper, a new resistive components measurement method termed as the remote nonsimultaneous resistive current measurement method (RNS) for MOSAs online condition monitoring is proposed. The RNS remotely and nonsimultaneously measures all types of resistive current parameters of MOSAs by employing the remote nonsimultaneous phase difference measurement method and noninteger harmonic analysis algorithm. The simulation and application results indicate that the proposed method accurately measures various parameters of MOSAs under complicated field circumstances. [ABSTRACT FROM AUTHOR]

Current trends in high performance and embedded computing include design of increasingly complex hardware architectures with high parallelism, heterogeneous processing elements, and nonuniform communication resources. In order to take hardware and software design decisions, early evaluations of the system nonfunctional properties are needed. These evaluations of system efficiency require electronic system-level information on both algorithms and architecture. Contrary to algorithm models for which a major body of work has been conducted on defining formal models of computation (MoCs), architecture models from the literature are mostly empirical models from which reproducible experimentation requires the accompanying software. In this paper, a precise definition of a model of architecture (MoA) is proposed that focuses on reproducibility and abstraction and removes the overlap previously existing between the notions of MoA and MoC. A first MoA, called the linear system-level architecture model (LSLA), is presented. To demonstrate the generic nature of the proposed new architecture modeling concepts, we show that the LSLA model can be integrated flexibly with different MoCs. LSLA is then used to model the energy consumption of a state-of-the-art multiprocessor system-on-chip (MPSoC) when running an application described using the synchronous dataflow MoC. A method to automatically learn LSLA model parameters from platform measurements is introduced. Despite the high complexity of the underlying hardware and software, a simple LSLA model is demonstrated to estimate the energy consumption of the MPSoC with a fidelity of 86%. [ABSTRACT FROM AUTHOR]

The original Summed Area Table (SAT) structure is designed for handling 2D rectangular data. Due to the nature of spherical functions, the SAT structure cannot handle cube maps directly. This paper proposes a new SAT structure for cube maps and develops the corresponding lookup algorithm. Our formulation starts by considering a cube map as part of an auxiliary 3D function defined in a 3D rectangular space. We interpret the 2D integration process over the cube map surface as a 3D integration over the auxiliary 3D function. One may suggest that we can create a 3D SAT for this auxiliary function, and then use the 3D SAT to achieve the 3D integration. However, it is not practical to generate or store this special 3D SAT directly. This 3D SAT has some nice properties that allow us to store it in a storage-friendly data structure, namely Summed Area Cube Map (SACM). A SACM can be stored in a standard cube map texture. The lookup algorithm of our SACM structure can be implemented efficiently on current graphics hardware. In addition, the SACM structure inherits the favorable properties of the original SAT structure. [ABSTRACT FROM AUTHOR]

When solving network algebraic equations during power system transient stability simulations, the nonzero elements in the independent vector and the elements needed in the solution vector are determined by the location of source nodes. Different kinds of source nodes will have a different influence on the solution process of the network equations. According to these features of source nodes, sparsity techniques, more specifically, the node ordering algorithm and sparse vector method, are improved in this paper. A new heuristic ordering algorithm is proposed to enhance the efficiency of the sparse vector method by reducing the number of nodes in the factorization path set of source nodes while maintaining the sparsity of the factorized matrix. A multipath sparse vector method is proposed to avoid the unnecessary computation in the iterative solution process of the network equations, whereby the idea of the method is to form different paths for different types of source nodes. Different paths are used to solve different elements in the solution vector. The correctness and effectiveness of these improvements is proven by the simulation results of three practical power systems (up to 13 490 nodes) and six IEEE examples (up to 162 nodes). [ABSTRACT FROM AUTHOR]

Demand response (DR) programs incentivize consumers for using their elastic demand for demand shaping, supply-demand balancing, and other ancillary and network support services. This paper proposes a dynamic nonlinear pricing scheme for behind-the-meter distributed energy resources (DERs), such as residential batteries, plug-in electric vehicles, and smart appliances, participating in a DR program, based on distributed mechanism design concepts. The underlying method is a faithful mechanism that can overlay any tractable dual-decomposition algorithm that solves the DER management and coordination problem in a distributed fashion. Since electricity is a continuously produced and divisible commodity that cannot be invariably allocated or stored, the pricing scheme is designed to couple the negotiated allocations and the actual consumption in a proportionally fair way, which means that it assigns lower payments to consumers who require less energy and are more accurate in following these requirements. Specifically, this pricing scheme is based on the Lagrange multipliers, which are the byproducts of any dual-decomposition algorithm that solves the nonconvex DER management and coordination problem. As a result, and in contrast to most existing time-varying pricing schemes, this pricing strategy does not suffer from rebound peaks repercussions. Furthermore, the proposed distributed mechanism is proven to be asymptotically faithful, i.e., faithful when the number of households grows large, collusion proof and weakly budget balanced. [ABSTRACT FROM AUTHOR]

Distributed generators are integrated into microgrids or geographically distributed subsystems in active distribution networks and may belong to various entities. Centralized solutions cannot meet future requirements such as high resilience and privacy protection. Therefore, fully distributed economic dispatch (ED) methods are needed and most existing algorithms exhibit linear or sublinear convergence. This paper introduces an efficient fully distributed dynamic ED solution for minimizing photovoltaic power curtailment, based on a parallel primal-dual interior-point algorithm with a quasi-Newton technique. In this method, each area optimizes its own problem with limited information exchanged with its neighbors, and no central coordinator is needed. Based on a peer-to-peer communication paradigm, this method can achieve a global optimum while the privacy of each area is obliquely protected. Numerical tests demonstrated that the algorithm outperforms gradient-based methods such as ADMM and maintains stability under a partial communication failure. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of channel estimation and decoding in environments that exhibit time-varying block-memoryless impulsive noise. In order to perform the estimation in continuous value space and the decoding in discrete value space jointly, a novel message passing framework is proposed that combines the sampling-importance resampling (SIR) estimator and the LDPC sum-product decoder in an iterative manner. The resampling process within the framework is further improved based on the asymptotic performance analysis for mismatched decoding. The simulation results prove the validity of the enhanced SIR estimator in the proposed method and demonstrate our method can achieve better performance than the LDPC decoder with quantile estimator. [ABSTRACT FROM AUTHOR]

In-circuit frequency response measurement of power converters enables verification of the control loop and periodic measurement during normal operation of the converter. This enables addition of diagnostics and monitoring functions without additional cost. The disturbance injected for frequency response measurement causes output of the converter to be disturbed, which is undesirable during normal converter operation. This paper presents an auto injection control algorithm by which the disturbance amplitude is controlled while maintaining good measurement fidelity. A voltage source inverter is selected as a case study to test the proposed algorithm, in which both current and voltage loop are verified using in-circuit frequency response analysis measurement. [ABSTRACT FROM PUBLISHER]

Matching-based algorithms have been commonly used in planar object tracking. They often model a planar object as a set of keypoints, and then find correspondences between keypoint sets via descriptor matching. In previous work, unary constraints on appearances or locations are usually used to guide the matching. However, these approaches rarely utilize structure information of the object, and are thus suffering from various perturbation factors. In this paper, we proposed a graph-based tracker, named Gracker, which is able to fully explore the structure information of the object to enhance tracking performance. We model a planar object as a graph, instead of a simple collection of keypoints, to represent its structure. Then, we reformulate tracking as a sequential graph matching process, which establishes keypoint correspondence in a geometric graph matching manner. For evaluation, we compare the proposed Gracker with state-of-the-art planar object trackers on three benchmark datasets: two public ones and a newly collected one. Experimental results show that Gracker achieves robust tracking results against various environmental variations, and outperforms other algorithms in general on the datasets. [ABSTRACT FROM PUBLISHER]

Matrix factorization is a popular approach for large-scale matrix completion. The optimization formulation based on matrix factorization, even with huge size, can be solved very efficiently through the standard optimization algorithms in practice. However, due to the non-convexity caused by the factorization model, there is a limited theoretical understanding of whether these algorithms will generate a good solution. In this paper, we establish a theoretical guarantee for the factorization-based formulation to correctly recover the underlying low-rank matrix. In particular, we show that under similar conditions to those in previous works, many standard optimization algorithms converge to the global optima of a factorization-based formulation and recover the true low-rank matrix. We study the local geometry of a properly regularized objective and prove that any stationary point in a certain local region is globally optimal. A major difference of this paper from the existing results is that we do not need resampling (i.e., using independent samples at each iteration) in either the algorithm or its analysis. [ABSTRACT FROM PUBLISHER]

This paper deals with a specific explicit design of Generalized Predictive Control (GPC) for speed and position of three-phase Permanent Magnet Synchronous Motors (PMSM). Predictive algorithms are designed with single and double integrations with respect to step and ramp reference signals. Needful field weakening and current limitation are solved by specific procedures based on a local indirect tuning/amplification of the relevant GPC parameters. The proposed solution due to low computational demands is suitable for real applications. Designed algorithms and procedures are demonstrated by figures and oscillogram screenshots of representative variables measured on the 10.7 kW PMSM drive. [ABSTRACT FROM PUBLISHER]

Many edge computing systems rely on virtual machines (VMs) to deliver their services. It is challenging, however, to deploy the virtualization mechanisms on edge computing hardware infrastructures. In this paper, we introduce the engineering and research trends of achieving efficient VM management in edge computing. We elaborate on: 1) the virtualization frameworks for edge computing developed in both the industry and the academia; 2) the virtualization techniques tailored for edge computing; 3) the placement and scheduling algorithms optimized for edge computing; and 4) the research problems in security related to virtualization of edge computing. [ABSTRACT FROM AUTHOR]

A recent paper titled “On the Problem of the Automated Design of Large-Scaled Robot Skin” (Anghinolfi et al., 2013) published in the IEEE Transactions on Automation Science and Engineering addressed the problem of covering the surface of a humanoid robot with the largest number of nonoverlapping equilateral triangular sensor modules. The problem is eventually approximated by a simpler one: how to find the placement of a given polygon P on an equilateral triangular grid G that contains the largest number of the grid triangles. In this paper, we show how to improve the efficiency of the algorithm presented in that paper. Further, we show that the general problem of filling P with the largest number of disjoint equilateral triangles (all entirely contained in P and all of the same size) is not equivalent to that of finding an optimal placement of P on G. Using this result, we propose an improved heuristic for the original problem of covering the skin of a robot with the largest number of triangular sensor modules. [ABSTRACT FROM PUBLISHER]